Customization has traditionally been seen as a human driven process and personalization a largely algorithmic one, they look set to collide in the notion of data driven manufacturing that is core to the vision of Industry 4.0. I explore the idea in which entire ecologies of products to better meet an individual’s needs would be customized by the combination of digital services that profile consumers’ interests, tastes, abilities and contexts and AMT. Specifically, a combination of motion capture data about dancers so as to drive AMT, specifically additive manufacturing, to customize lower artificial limbs for dancers.

We can now ubiquitously track aspects of our objective physical wellbeing whilst fulfilling the activities of daily life; this can lead to positive behaviour change and life improvements. If we had the ability to monitor our cognitive activity in daily life, similar benefits may be procured. Thus, this project aims to investigate what it means to track mental workload in daily life. Based on the assumption that a means to accurately interpret cognitive activity in an uncontrolled environment will one day be developed, we firstly aim to estimate mental workload levels ‘in the wild’ in order to establish how users consider this data and thus determine an optimal method of communicating mental workload data back to the user. An aim will also be to understand how mental workload can be contextualised.

Mixed Reality Laboratory

University of Nottingham
School of Computer Science
Nottingham, NG8 1BB